Both partial and non-partial correlation methods have been utilized by researchers to construct dense-array electroencephalographic (dEEG) networks. Similarly, researchers have been using different protocols to preprocess data and minimize spurious correlations. This methodological study examined the extent to which the dEEG connectivity networks computed by the non-partial, first-order partial, and (N-2)-order/254th-order partial correlation methods resemble each other, with two autoregressive integrative moving average (ARIMA) preprocessing models and three sample lengths being taken into consideration. Data were collected from two volunteers during the last 60 seconds of the second and fourth rapid-eye-movement epochs using a 256-channel electroencephalographic system and prior to correlation analyses, were preprocessed by either ARIMA (40, 1, 1) or ARIMA (20, 1, 1) transformation. The analyses demonstrate that the partial method, even at the first-order level, can substantially suppress the overall degree of connectivity in a network. Nevertheless, the moderate-to-large rank-order correlation values comparing the similarities between the three network-construction methods casts doubt on the supposition that a network built upon partial correlations is fundamentally distinguished from that derived from non-partial correlations.

Yu, C. K.-C., & Li, W.-O. (2018). A fundamental question about the application of high-density electroencephalography and time-series analysis in examining synchronous networks during sleep – Does the use of different referencing and data preprocessing methods really matter? Sleep and Hypnosis, 20, 67-84.